Merge branch 'dev' into torch
This commit is contained in:
AUTOMATIC1111
GitHub
commit
f54cd3f158
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@ -8,7 +8,7 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork):
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def activate(self, p, params_list):
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additional = shared.opts.sd_lora
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if additional != "" and additional in lora.available_loras and len([x for x in params_list if x.items[0] == additional]) == 0:
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if additional != "None" and additional in lora.available_loras and len([x for x in params_list if x.items[0] == additional]) == 0:
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p.all_prompts = [x + f"<lora:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
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params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
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@ -52,5 +52,5 @@ script_callbacks.on_before_ui(before_ui)
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shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), {
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"sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": [""] + [x for x in lora.available_loras]}, refresh=lora.list_available_loras),
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"sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in lora.available_loras]}, refresh=lora.list_available_loras),
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}))
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@ -5,11 +5,15 @@ import traceback
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import PIL.Image
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import numpy as np
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import torch
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from tqdm import tqdm
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from basicsr.utils.download_util import load_file_from_url
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import modules.upscaler
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from modules import devices, modelloader
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from scunet_model_arch import SCUNet as net
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from modules.shared import opts
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from modules import images
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class UpscalerScuNET(modules.upscaler.Upscaler):
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@ -42,28 +46,78 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
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scalers.append(scaler_data2)
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self.scalers = scalers
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def do_upscale(self, img: PIL.Image, selected_file):
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@staticmethod
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@torch.no_grad()
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def tiled_inference(img, model):
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# test the image tile by tile
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h, w = img.shape[2:]
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tile = opts.SCUNET_tile
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tile_overlap = opts.SCUNET_tile_overlap
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if tile == 0:
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return model(img)
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device = devices.get_device_for('scunet')
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assert tile % 8 == 0, "tile size should be a multiple of window_size"
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sf = 1
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stride = tile - tile_overlap
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h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
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w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
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E = torch.zeros(1, 3, h * sf, w * sf, dtype=img.dtype, device=device)
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W = torch.zeros_like(E, dtype=devices.dtype, device=device)
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with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="ScuNET tiles") as pbar:
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for h_idx in h_idx_list:
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for w_idx in w_idx_list:
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in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
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out_patch = model(in_patch)
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out_patch_mask = torch.ones_like(out_patch)
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E[
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..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
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].add_(out_patch)
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W[
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..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
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].add_(out_patch_mask)
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pbar.update(1)
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output = E.div_(W)
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return output
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def do_upscale(self, img: PIL.Image.Image, selected_file):
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torch.cuda.empty_cache()
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model = self.load_model(selected_file)
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if model is None:
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print(f"ScuNET: Unable to load model from {selected_file}", file=sys.stderr)
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return img
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device = devices.get_device_for('scunet')
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img = np.array(img)
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img = img[:, :, ::-1]
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img = np.moveaxis(img, 2, 0) / 255
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img = torch.from_numpy(img).float()
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img = img.unsqueeze(0).to(device)
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tile = opts.SCUNET_tile
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h, w = img.height, img.width
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np_img = np.array(img)
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np_img = np_img[:, :, ::-1] # RGB to BGR
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np_img = np_img.transpose((2, 0, 1)) / 255 # HWC to CHW
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torch_img = torch.from_numpy(np_img).float().unsqueeze(0).to(device) # type: ignore
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with torch.no_grad():
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output = model(img)
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output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
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output = 255. * np.moveaxis(output, 0, 2)
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output = output.astype(np.uint8)
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output = output[:, :, ::-1]
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if tile > h or tile > w:
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_img = torch.zeros(1, 3, max(h, tile), max(w, tile), dtype=torch_img.dtype, device=torch_img.device)
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_img[:, :, :h, :w] = torch_img # pad image
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torch_img = _img
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torch_output = self.tiled_inference(torch_img, model).squeeze(0)
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torch_output = torch_output[:, :h * 1, :w * 1] # remove padding, if any
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np_output: np.ndarray = torch_output.float().cpu().clamp_(0, 1).numpy()
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del torch_img, torch_output
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torch.cuda.empty_cache()
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return PIL.Image.fromarray(output, 'RGB')
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output = np_output.transpose((1, 2, 0)) # CHW to HWC
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output = output[:, :, ::-1] # BGR to RGB
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return PIL.Image.fromarray((output * 255).astype(np.uint8))
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def load_model(self, path: str):
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device = devices.get_device_for('scunet')
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@ -84,4 +138,3 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
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model = model.to(device)
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return model
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@ -161,14 +161,6 @@ addContextMenuEventListener = initResponse[2];
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appendContextMenuOption('#img2img_interrupt','Cancel generate forever',cancelGenerateForever)
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appendContextMenuOption('#img2img_generate', 'Cancel generate forever',cancelGenerateForever)
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appendContextMenuOption('#roll','Roll three',
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function(){
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let rollbutton = get_uiCurrentTabContent().querySelector('#roll');
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setTimeout(function(){rollbutton.click()},100)
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setTimeout(function(){rollbutton.click()},200)
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setTimeout(function(){rollbutton.click()},300)
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}
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)
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})();
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//End example Context Menu Items
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@ -17,7 +17,7 @@ function keyupEditAttention(event){
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// Find opening parenthesis around current cursor
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const before = text.substring(0, selectionStart);
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let beforeParen = before.lastIndexOf(OPEN);
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if (beforeParen == -1) return false;
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if (beforeParen == -1) return false;
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let beforeParenClose = before.lastIndexOf(CLOSE);
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while (beforeParenClose !== -1 && beforeParenClose > beforeParen) {
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beforeParen = before.lastIndexOf(OPEN, beforeParen - 1);
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@ -27,7 +27,7 @@ function keyupEditAttention(event){
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// Find closing parenthesis around current cursor
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const after = text.substring(selectionStart);
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let afterParen = after.indexOf(CLOSE);
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if (afterParen == -1) return false;
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if (afterParen == -1) return false;
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let afterParenOpen = after.indexOf(OPEN);
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while (afterParenOpen !== -1 && afterParen > afterParenOpen) {
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afterParen = after.indexOf(CLOSE, afterParen + 1);
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@ -43,10 +43,28 @@ function keyupEditAttention(event){
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target.setSelectionRange(selectionStart, selectionEnd);
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return true;
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}
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function selectCurrentWord(){
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if (selectionStart !== selectionEnd) return false;
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const delimiters = opts.keyedit_delimiters + " \r\n\t";
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// seek backward until to find beggining
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while (!delimiters.includes(text[selectionStart - 1]) && selectionStart > 0) {
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selectionStart--;
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}
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// seek forward to find end
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while (!delimiters.includes(text[selectionEnd]) && selectionEnd < text.length) {
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selectionEnd++;
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}
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// If the user hasn't selected anything, let's select their current parenthesis block
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if(! selectCurrentParenthesisBlock('<', '>')){
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selectCurrentParenthesisBlock('(', ')')
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target.setSelectionRange(selectionStart, selectionEnd);
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return true;
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}
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// If the user hasn't selected anything, let's select their current parenthesis block or word
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if (!selectCurrentParenthesisBlock('<', '>') && !selectCurrentParenthesisBlock('(', ')')) {
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selectCurrentWord();
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}
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event.preventDefault();
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@ -81,7 +99,13 @@ function keyupEditAttention(event){
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weight = parseFloat(weight.toPrecision(12));
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if(String(weight).length == 1) weight += ".0"
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text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + 1 + end - 1);
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if (closeCharacter == ')' && weight == 1) {
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text = text.slice(0, selectionStart - 1) + text.slice(selectionStart, selectionEnd) + text.slice(selectionEnd + 5);
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selectionStart--;
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selectionEnd--;
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} else {
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text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + 1 + end - 1);
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}
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target.focus();
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target.value = text;
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@ -93,4 +117,4 @@ function keyupEditAttention(event){
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addEventListener('keydown', (event) => {
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keyupEditAttention(event);
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});
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});
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@ -16,7 +16,7 @@ onUiUpdate(function(){
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let modalObserver = new MutationObserver(function(mutations) {
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mutations.forEach(function(mutationRecord) {
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let selectedTab = gradioApp().querySelector('#tabs div button.bg-white')?.innerText
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let selectedTab = gradioApp().querySelector('#tabs div button')?.innerText
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if (mutationRecord.target.style.display === 'none' && selectedTab === 'txt2img' || selectedTab === 'img2img')
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gradioApp().getElementById(selectedTab+"_generation_info_button").click()
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});
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@ -251,8 +251,11 @@ document.addEventListener("DOMContentLoaded", function() {
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modal.appendChild(modalNext)
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gradioApp().appendChild(modal)
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try {
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gradioApp().appendChild(modal);
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} catch (e) {
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gradioApp().body.appendChild(modal);
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}
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document.body.appendChild(modal);
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@ -138,7 +138,7 @@ function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgre
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return
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}
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if(elapsedFromStart > 5 && !res.queued && !res.active){
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if(elapsedFromStart > 40 && !res.queued && !res.active){
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removeProgressBar()
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return
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}
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@ -6,7 +6,6 @@ import uvicorn
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import gradio as gr
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from threading import Lock
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from io import BytesIO
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from gradio.processing_utils import decode_base64_to_file
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from fastapi import APIRouter, Depends, FastAPI, Request, Response
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from fastapi.security import HTTPBasic, HTTPBasicCredentials
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from fastapi.exceptions import HTTPException
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@ -395,16 +394,11 @@ class Api:
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def extras_batch_images_api(self, req: ExtrasBatchImagesRequest):
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reqDict = setUpscalers(req)
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def prepareFiles(file):
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file = decode_base64_to_file(file.data, file_path=file.name)
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file.orig_name = file.name
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return file
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reqDict['image_folder'] = list(map(prepareFiles, reqDict['imageList']))
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reqDict.pop('imageList')
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image_list = reqDict.pop('imageList', [])
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image_folder = [decode_base64_to_image(x.data) for x in image_list]
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with self.queue_lock:
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result = postprocessing.run_extras(extras_mode=1, image="", input_dir="", output_dir="", save_output=False, **reqDict)
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result = postprocessing.run_extras(extras_mode=1, image_folder=image_folder, image="", input_dir="", output_dir="", save_output=False, **reqDict)
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return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
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@ -92,14 +92,18 @@ def cond_cast_float(input):
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def randn(seed, shape):
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from modules.shared import opts
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torch.manual_seed(seed)
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if device.type == 'mps':
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if opts.randn_source == "CPU" or device.type == 'mps':
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return torch.randn(shape, device=cpu).to(device)
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return torch.randn(shape, device=device)
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def randn_without_seed(shape):
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if device.type == 'mps':
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from modules.shared import opts
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if opts.randn_source == "CPU" or device.type == 'mps':
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return torch.randn(shape, device=cpu).to(device)
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return torch.randn(shape, device=device)
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@ -9,7 +9,7 @@ class ExtraNetworkHypernet(extra_networks.ExtraNetwork):
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def activate(self, p, params_list):
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additional = shared.opts.sd_hypernetwork
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if additional != "" and additional in shared.hypernetworks and len([x for x in params_list if x.items[0] == additional]) == 0:
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if additional != "None" and additional in shared.hypernetworks and len([x for x in params_list if x.items[0] == additional]) == 0:
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p.all_prompts = [x + f"<hypernet:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
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params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
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@ -284,6 +284,10 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
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restore_old_hires_fix_params(res)
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# Missing RNG means the default was set, which is GPU RNG
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if "RNG" not in res:
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res["RNG"] = "GPU"
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return res
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@ -304,6 +308,7 @@ infotext_to_setting_name_mapping = [
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('UniPC skip type', 'uni_pc_skip_type'),
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('UniPC order', 'uni_pc_order'),
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('UniPC lower order final', 'uni_pc_lower_order_final'),
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('RNG', 'randn_source'),
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]
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|
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@ -352,6 +352,7 @@ class FilenameGenerator:
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'prompt_no_styles': lambda self: self.prompt_no_style(),
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'prompt_spaces': lambda self: sanitize_filename_part(self.prompt, replace_spaces=False),
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'prompt_words': lambda self: self.prompt_words(),
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'clip_skip': lambda self: opts.data["CLIP_stop_at_last_layers"],
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}
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default_time_format = '%Y%m%d%H%M%S'
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|
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@ -151,7 +151,7 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
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override_settings=override_settings,
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)
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p.scripts = modules.scripts.scripts_txt2img
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p.scripts = modules.scripts.scripts_img2img
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p.script_args = args
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if shared.cmd_opts.enable_console_prompts:
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|
|
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|
@ -32,7 +32,7 @@ def download_default_clip_interrogate_categories(content_dir):
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category_types = ["artists", "flavors", "mediums", "movements"]
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try:
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os.makedirs(tmpdir)
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os.makedirs(tmpdir, exist_ok=True)
|
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for category_type in category_types:
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torch.hub.download_url_to_file(f"https://raw.githubusercontent.com/pharmapsychotic/clip-interrogator/main/clip_interrogator/data/{category_type}.txt", os.path.join(tmpdir, f"{category_type}.txt"))
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os.rename(tmpdir, content_dir)
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@ -41,7 +41,7 @@ def download_default_clip_interrogate_categories(content_dir):
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errors.display(e, "downloading default CLIP interrogate categories")
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finally:
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if os.path.exists(tmpdir):
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os.remove(tmpdir)
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os.removedirs(tmpdir)
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|
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|
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class InterrogateModels:
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|
|
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@ -18,9 +18,15 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,
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|
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if extras_mode == 1:
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for img in image_folder:
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image = Image.open(img)
|
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if isinstance(img, Image.Image):
|
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image = img
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fn = ''
|
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else:
|
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image = Image.open(os.path.abspath(img.name))
|
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fn = os.path.splitext(img.orig_name)[0]
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|
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image_data.append(image)
|
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image_names.append(os.path.splitext(img.orig_name)[0])
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image_names.append(fn)
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elif extras_mode == 2:
|
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assert not shared.cmd_opts.hide_ui_dir_config, '--hide-ui-dir-config option must be disabled'
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assert input_dir, 'input directory not selected'
|
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|
|
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|
@ -3,6 +3,7 @@ import math
|
|||
import os
|
||||
import sys
|
||||
import warnings
|
||||
import hashlib
|
||||
|
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import torch
|
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import numpy as np
|
||||
|
|
@ -476,6 +477,8 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
|
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"Conditional mask weight": getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None,
|
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"Clip skip": None if clip_skip <= 1 else clip_skip,
|
||||
"ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta,
|
||||
"Init image hash": getattr(p, 'init_img_hash', None),
|
||||
"RNG": (opts.randn_source if opts.randn_source != "GPU" else None)
|
||||
}
|
||||
|
||||
generation_params.update(p.extra_generation_params)
|
||||
|
|
@ -1007,6 +1010,12 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
|
|||
self.color_corrections = []
|
||||
imgs = []
|
||||
for img in self.init_images:
|
||||
|
||||
# Save init image
|
||||
if opts.save_init_img:
|
||||
self.init_img_hash = hashlib.md5(img.tobytes()).hexdigest()
|
||||
images.save_image(img, path=opts.outdir_init_images, basename=None, forced_filename=self.init_img_hash, save_to_dirs=False)
|
||||
|
||||
image = images.flatten(img, opts.img2img_background_color)
|
||||
|
||||
if crop_region is None and self.resize_mode != 3:
|
||||
|
|
|
|||
|
|
@ -60,3 +60,13 @@ def store_latent(decoded):
|
|||
|
||||
class InterruptedException(BaseException):
|
||||
pass
|
||||
|
||||
|
||||
if opts.randn_source == "CPU":
|
||||
import torchsde._brownian.brownian_interval
|
||||
|
||||
def torchsde_randn(size, dtype, device, seed):
|
||||
generator = torch.Generator(devices.cpu).manual_seed(int(seed))
|
||||
return torch.randn(size, dtype=dtype, device=devices.cpu, generator=generator).to(device)
|
||||
|
||||
torchsde._brownian.brownian_interval._randn = torchsde_randn
|
||||
|
|
|
|||
|
|
@ -190,7 +190,7 @@ class TorchHijack:
|
|||
if noise.shape == x.shape:
|
||||
return noise
|
||||
|
||||
if x.device.type == 'mps':
|
||||
if opts.randn_source == "CPU" or x.device.type == 'mps':
|
||||
return torch.randn_like(x, device=devices.cpu).to(x.device)
|
||||
else:
|
||||
return torch.randn_like(x)
|
||||
|
|
|
|||
|
|
@ -39,6 +39,7 @@ restricted_opts = {
|
|||
"outdir_grids",
|
||||
"outdir_txt2img_grids",
|
||||
"outdir_save",
|
||||
"outdir_init_images"
|
||||
}
|
||||
|
||||
ui_reorder_categories = [
|
||||
|
|
@ -253,6 +254,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
|
|||
"use_upscaler_name_as_suffix": OptionInfo(False, "Use upscaler name as filename suffix in the extras tab"),
|
||||
"save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
|
||||
"do_not_add_watermark": OptionInfo(False, "Do not add watermark to images"),
|
||||
"save_init_img": OptionInfo(False, "Save init images when using img2img"),
|
||||
|
||||
"temp_dir": OptionInfo("", "Directory for temporary images; leave empty for default"),
|
||||
"clean_temp_dir_at_start": OptionInfo(False, "Cleanup non-default temporary directory when starting webui"),
|
||||
|
|
@ -268,6 +270,7 @@ options_templates.update(options_section(('saving-paths', "Paths for saving"), {
|
|||
"outdir_txt2img_grids": OptionInfo("outputs/txt2img-grids", 'Output directory for txt2img grids', component_args=hide_dirs),
|
||||
"outdir_img2img_grids": OptionInfo("outputs/img2img-grids", 'Output directory for img2img grids', component_args=hide_dirs),
|
||||
"outdir_save": OptionInfo("log/images", "Directory for saving images using the Save button", component_args=hide_dirs),
|
||||
"outdir_init_images": OptionInfo("outputs/init-images", "Directory for saving init images when using img2img", component_args=hide_dirs),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('saving-to-dirs', "Saving to a directory"), {
|
||||
|
|
@ -283,6 +286,8 @@ options_templates.update(options_section(('upscaling', "Upscaling"), {
|
|||
"ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
|
||||
"realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": shared_items.realesrgan_models_names()}),
|
||||
"upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
|
||||
"SCUNET_tile": OptionInfo(256, "Tile size for SCUNET upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
|
||||
"SCUNET_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SCUNET upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('face-restoration', "Face restoration"), {
|
||||
|
|
@ -331,6 +336,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
|
|||
"comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
|
||||
"CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
|
||||
"upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
|
||||
"randn_source": OptionInfo("GPU", "Random number generator source. Changes seeds drastically. Use CPU to produce the same picture across different vidocard vendors.", gr.Radio, {"choices": ["GPU", "CPU"]}),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('compatibility', "Compatibility"), {
|
||||
|
|
@ -361,7 +367,7 @@ options_templates.update(options_section(('extra_networks', "Extra Networks"), {
|
|||
"extra_networks_card_width": OptionInfo(0, "Card width for Extra Networks (px)"),
|
||||
"extra_networks_card_height": OptionInfo(0, "Card height for Extra Networks (px)"),
|
||||
"extra_networks_add_text_separator": OptionInfo(" ", "Extra text to add before <...> when adding extra network to prompt"),
|
||||
"sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": [""] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
|
||||
"sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('ui', "User interface"), {
|
||||
|
|
@ -382,6 +388,7 @@ options_templates.update(options_section(('ui', "User interface"), {
|
|||
"dimensions_and_batch_together": OptionInfo(True, "Show Width/Height and Batch sliders in same row"),
|
||||
"keyedit_precision_attention": OptionInfo(0.1, "Ctrl+up/down precision when editing (attention:1.1)", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
|
||||
"keyedit_precision_extra": OptionInfo(0.05, "Ctrl+up/down precision when editing <extra networks:0.9>", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
|
||||
"keyedit_delimiters": OptionInfo(".,\/!?%^*;:{}=`~()", "Ctrl+up/down word delimiters"),
|
||||
"quicksettings": OptionInfo("sd_model_checkpoint", "Quicksettings list"),
|
||||
"hidden_tabs": OptionInfo([], "Hidden UI tabs (requires restart)", ui_components.DropdownMulti, lambda: {"choices": [x for x in tab_names]}),
|
||||
"ui_reorder": OptionInfo(", ".join(ui_reorder_categories), "txt2img/img2img UI item order"),
|
||||
|
|
|
|||
|
|
@ -171,8 +171,8 @@ def create_seed_inputs(target_interface):
|
|||
with FormRow(elem_id=target_interface + '_seed_row', variant="compact"):
|
||||
seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1, elem_id=target_interface + '_seed')
|
||||
seed.style(container=False)
|
||||
random_seed = ToolButton(random_symbol, elem_id=target_interface + '_random_seed')
|
||||
reuse_seed = ToolButton(reuse_symbol, elem_id=target_interface + '_reuse_seed')
|
||||
random_seed = ToolButton(random_symbol, elem_id=target_interface + '_random_seed', label='Random seed')
|
||||
reuse_seed = ToolButton(reuse_symbol, elem_id=target_interface + '_reuse_seed', label='Reuse seed')
|
||||
|
||||
seed_checkbox = gr.Checkbox(label='Extra', elem_id=target_interface + '_subseed_show', value=False)
|
||||
|
||||
|
|
@ -468,7 +468,7 @@ def create_ui():
|
|||
height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height")
|
||||
|
||||
with gr.Column(elem_id="txt2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
|
||||
res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn")
|
||||
res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn", label="Switch dims")
|
||||
|
||||
if opts.dimensions_and_batch_together:
|
||||
with gr.Column(elem_id="txt2img_column_batch"):
|
||||
|
|
@ -1204,7 +1204,7 @@ def create_ui():
|
|||
|
||||
with gr.Column(elem_id='ti_gallery_container'):
|
||||
ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
|
||||
ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4)
|
||||
ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(columns=4)
|
||||
ti_progress = gr.HTML(elem_id="ti_progress", value="")
|
||||
ti_outcome = gr.HTML(elem_id="ti_error", value="")
|
||||
|
||||
|
|
@ -1705,7 +1705,7 @@ def create_ui():
|
|||
if init_field is not None:
|
||||
init_field(saved_value)
|
||||
|
||||
if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number, gr.Dropdown] and x.visible:
|
||||
if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number, gr.Dropdown, ToolButton] and x.visible:
|
||||
apply_field(x, 'visible')
|
||||
|
||||
if type(x) == gr.Slider:
|
||||
|
|
|
|||
|
|
@ -125,7 +125,7 @@ Requested path was: {f}
|
|||
|
||||
with gr.Column(variant='panel', elem_id=f"{tabname}_results"):
|
||||
with gr.Group(elem_id=f"{tabname}_gallery_container"):
|
||||
result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(grid=4)
|
||||
result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(columns=4)
|
||||
|
||||
generation_info = None
|
||||
with gr.Column():
|
||||
|
|
|
|||
|
|
@ -13,7 +13,7 @@ def create_ui():
|
|||
extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil", elem_id="extras_image")
|
||||
|
||||
with gr.TabItem('Batch Process', elem_id="extras_batch_process_tab") as tab_batch:
|
||||
image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file", elem_id="extras_image_batch")
|
||||
image_batch = gr.Files(label="Batch Process", interactive=True, elem_id="extras_image_batch")
|
||||
|
||||
with gr.TabItem('Batch from Directory', elem_id="extras_batch_directory_tab") as tab_batch_dir:
|
||||
extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.", elem_id="extras_batch_input_dir")
|
||||
|
|
|
|||
|
|
@ -5,7 +5,7 @@ basicsr
|
|||
fonts
|
||||
font-roboto
|
||||
gfpgan
|
||||
gradio==3.23
|
||||
gradio==3.27
|
||||
invisible-watermark
|
||||
numpy
|
||||
omegaconf
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ transformers==4.25.1
|
|||
accelerate==0.18.0
|
||||
basicsr==1.4.2
|
||||
gfpgan==1.3.8
|
||||
gradio==3.23
|
||||
gradio==3.27
|
||||
numpy==1.23.5
|
||||
Pillow==9.4.0
|
||||
realesrgan==0.3.0
|
||||
|
|
|
|||
|
|
@ -1,9 +1,40 @@
|
|||
import modules.scripts as scripts
|
||||
import gradio as gr
|
||||
import ast
|
||||
import copy
|
||||
|
||||
from modules.processing import Processed
|
||||
from modules.shared import opts, cmd_opts, state
|
||||
|
||||
|
||||
def convertExpr2Expression(expr):
|
||||
expr.lineno = 0
|
||||
expr.col_offset = 0
|
||||
result = ast.Expression(expr.value, lineno=0, col_offset = 0)
|
||||
|
||||
return result
|
||||
|
||||
|
||||
def exec_with_return(code, module):
|
||||
"""
|
||||
like exec() but can return values
|
||||
https://stackoverflow.com/a/52361938/5862977
|
||||
"""
|
||||
code_ast = ast.parse(code)
|
||||
|
||||
init_ast = copy.deepcopy(code_ast)
|
||||
init_ast.body = code_ast.body[:-1]
|
||||
|
||||
last_ast = copy.deepcopy(code_ast)
|
||||
last_ast.body = code_ast.body[-1:]
|
||||
|
||||
exec(compile(init_ast, "<ast>", "exec"), module.__dict__)
|
||||
if type(last_ast.body[0]) == ast.Expr:
|
||||
return eval(compile(convertExpr2Expression(last_ast.body[0]), "<ast>", "eval"), module.__dict__)
|
||||
else:
|
||||
exec(compile(last_ast, "<ast>", "exec"), module.__dict__)
|
||||
|
||||
|
||||
class Script(scripts.Script):
|
||||
|
||||
def title(self):
|
||||
|
|
@ -13,12 +44,23 @@ class Script(scripts.Script):
|
|||
return cmd_opts.allow_code
|
||||
|
||||
def ui(self, is_img2img):
|
||||
code = gr.Textbox(label="Python code", lines=1, elem_id=self.elem_id("code"))
|
||||
example = """from modules.processing import process_images
|
||||
|
||||
return [code]
|
||||
p.width = 768
|
||||
p.height = 768
|
||||
p.batch_size = 2
|
||||
p.steps = 10
|
||||
|
||||
return process_images(p)
|
||||
"""
|
||||
|
||||
|
||||
def run(self, p, code):
|
||||
code = gr.Code(value=example, language="python", label="Python code", elem_id=self.elem_id("code"))
|
||||
indent_level = gr.Number(label='Indent level', value=2, precision=0, elem_id=self.elem_id("indent_level"))
|
||||
|
||||
return [code, indent_level]
|
||||
|
||||
def run(self, p, code, indent_level):
|
||||
assert cmd_opts.allow_code, '--allow-code option must be enabled'
|
||||
|
||||
display_result_data = [[], -1, ""]
|
||||
|
|
@ -29,13 +71,20 @@ class Script(scripts.Script):
|
|||
display_result_data[2] = i
|
||||
|
||||
from types import ModuleType
|
||||
compiled = compile(code, '', 'exec')
|
||||
module = ModuleType("testmodule")
|
||||
module.__dict__.update(globals())
|
||||
module.p = p
|
||||
module.display = display
|
||||
exec(compiled, module.__dict__)
|
||||
|
||||
indent = " " * indent_level
|
||||
indented = code.replace('\n', '\n' + indent)
|
||||
body = f"""def __webuitemp__():
|
||||
{indent}{indented}
|
||||
__webuitemp__()"""
|
||||
|
||||
result = exec_with_return(body, module)
|
||||
|
||||
if isinstance(result, Processed):
|
||||
return result
|
||||
|
||||
return Processed(p, *display_result_data)
|
||||
|
||||
|
||||
|
|
@ -4,8 +4,8 @@ import numpy as np
|
|||
from modules import scripts_postprocessing, shared
|
||||
import gradio as gr
|
||||
|
||||
from modules.ui_components import FormRow
|
||||
|
||||
from modules.ui_components import FormRow, ToolButton
|
||||
from modules.ui import switch_values_symbol
|
||||
|
||||
upscale_cache = {}
|
||||
|
||||
|
|
@ -25,9 +25,12 @@ class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing):
|
|||
|
||||
with gr.TabItem('Scale to', elem_id="extras_scale_to_tab") as tab_scale_to:
|
||||
with FormRow():
|
||||
upscaling_resize_w = gr.Number(label="Width", value=512, precision=0, elem_id="extras_upscaling_resize_w")
|
||||
upscaling_resize_h = gr.Number(label="Height", value=512, precision=0, elem_id="extras_upscaling_resize_h")
|
||||
upscaling_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id="extras_upscaling_crop")
|
||||
with gr.Column(elem_id="upscaling_column_size", scale=4):
|
||||
upscaling_resize_w = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="extras_upscaling_resize_w")
|
||||
upscaling_resize_h = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="extras_upscaling_resize_h")
|
||||
with gr.Column(elem_id="upscaling_dimensions_row", scale=1, elem_classes="dimensions-tools"):
|
||||
upscaling_res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="upscaling_res_switch_btn")
|
||||
upscaling_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id="extras_upscaling_crop")
|
||||
|
||||
with FormRow():
|
||||
extras_upscaler_1 = gr.Dropdown(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
|
||||
|
|
@ -36,6 +39,7 @@ class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing):
|
|||
extras_upscaler_2 = gr.Dropdown(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
|
||||
extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=0.0, elem_id="extras_upscaler_2_visibility")
|
||||
|
||||
upscaling_res_switch_btn.click(lambda w, h: (h, w), inputs=[upscaling_resize_w, upscaling_resize_h], outputs=[upscaling_resize_w, upscaling_resize_h], show_progress=False)
|
||||
tab_scale_by.select(fn=lambda: 0, inputs=[], outputs=[selected_tab])
|
||||
tab_scale_to.select(fn=lambda: 1, inputs=[], outputs=[selected_tab])
|
||||
|
||||
|
|
|
|||
|
|
@ -374,16 +374,19 @@ class Script(scripts.Script):
|
|||
with gr.Row():
|
||||
x_type = gr.Dropdown(label="X type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[1].label, type="index", elem_id=self.elem_id("x_type"))
|
||||
x_values = gr.Textbox(label="X values", lines=1, elem_id=self.elem_id("x_values"))
|
||||
x_values_dropdown = gr.Dropdown(label="X values",visible=False,multiselect=True,interactive=True)
|
||||
fill_x_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_x_tool_button", visible=False)
|
||||
|
||||
with gr.Row():
|
||||
y_type = gr.Dropdown(label="Y type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[0].label, type="index", elem_id=self.elem_id("y_type"))
|
||||
y_values = gr.Textbox(label="Y values", lines=1, elem_id=self.elem_id("y_values"))
|
||||
y_values_dropdown = gr.Dropdown(label="Y values",visible=False,multiselect=True,interactive=True)
|
||||
fill_y_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_y_tool_button", visible=False)
|
||||
|
||||
with gr.Row():
|
||||
z_type = gr.Dropdown(label="Z type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[0].label, type="index", elem_id=self.elem_id("z_type"))
|
||||
z_values = gr.Textbox(label="Z values", lines=1, elem_id=self.elem_id("z_values"))
|
||||
z_values_dropdown = gr.Dropdown(label="Z values",visible=False,multiselect=True,interactive=True)
|
||||
fill_z_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_z_tool_button", visible=False)
|
||||
|
||||
with gr.Row(variant="compact", elem_id="axis_options"):
|
||||
|
|
@ -401,54 +404,74 @@ class Script(scripts.Script):
|
|||
swap_yz_axes_button = gr.Button(value="Swap Y/Z axes", elem_id="yz_grid_swap_axes_button")
|
||||
swap_xz_axes_button = gr.Button(value="Swap X/Z axes", elem_id="xz_grid_swap_axes_button")
|
||||
|
||||
def swap_axes(axis1_type, axis1_values, axis2_type, axis2_values):
|
||||
return self.current_axis_options[axis2_type].label, axis2_values, self.current_axis_options[axis1_type].label, axis1_values
|
||||
def swap_axes(axis1_type, axis1_values, axis1_values_dropdown, axis2_type, axis2_values, axis2_values_dropdown):
|
||||
return self.current_axis_options[axis2_type].label, axis2_values, axis2_values_dropdown, self.current_axis_options[axis1_type].label, axis1_values, axis1_values_dropdown
|
||||
|
||||
xy_swap_args = [x_type, x_values, y_type, y_values]
|
||||
xy_swap_args = [x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown]
|
||||
swap_xy_axes_button.click(swap_axes, inputs=xy_swap_args, outputs=xy_swap_args)
|
||||
yz_swap_args = [y_type, y_values, z_type, z_values]
|
||||
yz_swap_args = [y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown]
|
||||
swap_yz_axes_button.click(swap_axes, inputs=yz_swap_args, outputs=yz_swap_args)
|
||||
xz_swap_args = [x_type, x_values, z_type, z_values]
|
||||
xz_swap_args = [x_type, x_values, x_values_dropdown, z_type, z_values, z_values_dropdown]
|
||||
swap_xz_axes_button.click(swap_axes, inputs=xz_swap_args, outputs=xz_swap_args)
|
||||
|
||||
def fill(x_type):
|
||||
axis = self.current_axis_options[x_type]
|
||||
return ", ".join(axis.choices()) if axis.choices else gr.update()
|
||||
return axis.choices() if axis.choices else gr.update()
|
||||
|
||||
fill_x_button.click(fn=fill, inputs=[x_type], outputs=[x_values])
|
||||
fill_y_button.click(fn=fill, inputs=[y_type], outputs=[y_values])
|
||||
fill_z_button.click(fn=fill, inputs=[z_type], outputs=[z_values])
|
||||
fill_x_button.click(fn=fill, inputs=[x_type], outputs=[x_values_dropdown])
|
||||
fill_y_button.click(fn=fill, inputs=[y_type], outputs=[y_values_dropdown])
|
||||
fill_z_button.click(fn=fill, inputs=[z_type], outputs=[z_values_dropdown])
|
||||
|
||||
def select_axis(x_type):
|
||||
return gr.Button.update(visible=self.current_axis_options[x_type].choices is not None)
|
||||
def select_axis(axis_type,axis_values_dropdown):
|
||||
choices = self.current_axis_options[axis_type].choices
|
||||
has_choices = choices is not None
|
||||
current_values = axis_values_dropdown
|
||||
if has_choices:
|
||||
choices = choices()
|
||||
if isinstance(current_values,str):
|
||||
current_values = current_values.split(",")
|
||||
current_values = list(filter(lambda x: x in choices, current_values))
|
||||
return gr.Button.update(visible=has_choices),gr.Textbox.update(visible=not has_choices),gr.update(choices=choices if has_choices else None,visible=has_choices,value=current_values)
|
||||
|
||||
x_type.change(fn=select_axis, inputs=[x_type], outputs=[fill_x_button])
|
||||
y_type.change(fn=select_axis, inputs=[y_type], outputs=[fill_y_button])
|
||||
z_type.change(fn=select_axis, inputs=[z_type], outputs=[fill_z_button])
|
||||
x_type.change(fn=select_axis, inputs=[x_type,x_values_dropdown], outputs=[fill_x_button,x_values,x_values_dropdown])
|
||||
y_type.change(fn=select_axis, inputs=[y_type,y_values_dropdown], outputs=[fill_y_button,y_values,y_values_dropdown])
|
||||
z_type.change(fn=select_axis, inputs=[z_type,z_values_dropdown], outputs=[fill_z_button,z_values,z_values_dropdown])
|
||||
|
||||
def get_dropdown_update_from_params(axis,params):
|
||||
val_key = axis + " Values"
|
||||
vals = params.get(val_key,"")
|
||||
valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals))) if x]
|
||||
return gr.update(value = valslist)
|
||||
|
||||
self.infotext_fields = (
|
||||
(x_type, "X Type"),
|
||||
(x_values, "X Values"),
|
||||
(x_values_dropdown, lambda params:get_dropdown_update_from_params("X",params)),
|
||||
(y_type, "Y Type"),
|
||||
(y_values, "Y Values"),
|
||||
(y_values_dropdown, lambda params:get_dropdown_update_from_params("Y",params)),
|
||||
(z_type, "Z Type"),
|
||||
(z_values, "Z Values"),
|
||||
(z_values_dropdown, lambda params:get_dropdown_update_from_params("Z",params)),
|
||||
)
|
||||
|
||||
return [x_type, x_values, y_type, y_values, z_type, z_values, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size]
|
||||
return [x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size]
|
||||
|
||||
def run(self, p, x_type, x_values, y_type, y_values, z_type, z_values, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size):
|
||||
def run(self, p, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size):
|
||||
if not no_fixed_seeds:
|
||||
modules.processing.fix_seed(p)
|
||||
|
||||
if not opts.return_grid:
|
||||
p.batch_size = 1
|
||||
|
||||
def process_axis(opt, vals):
|
||||
def process_axis(opt, vals, vals_dropdown):
|
||||
if opt.label == 'Nothing':
|
||||
return [0]
|
||||
|
||||
valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals))) if x]
|
||||
if opt.choices is not None:
|
||||
valslist = vals_dropdown
|
||||
else:
|
||||
valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals))) if x]
|
||||
|
||||
if opt.type == int:
|
||||
valslist_ext = []
|
||||
|
|
@ -506,13 +529,19 @@ class Script(scripts.Script):
|
|||
return valslist
|
||||
|
||||
x_opt = self.current_axis_options[x_type]
|
||||
xs = process_axis(x_opt, x_values)
|
||||
if x_opt.choices is not None:
|
||||
x_values = ",".join(x_values_dropdown)
|
||||
xs = process_axis(x_opt, x_values, x_values_dropdown)
|
||||
|
||||
y_opt = self.current_axis_options[y_type]
|
||||
ys = process_axis(y_opt, y_values)
|
||||
if y_opt.choices is not None:
|
||||
y_values = ",".join(y_values_dropdown)
|
||||
ys = process_axis(y_opt, y_values, y_values_dropdown)
|
||||
|
||||
z_opt = self.current_axis_options[z_type]
|
||||
zs = process_axis(z_opt, z_values)
|
||||
if z_opt.choices is not None:
|
||||
z_values = ",".join(z_values_dropdown)
|
||||
zs = process_axis(z_opt, z_values, z_values_dropdown)
|
||||
|
||||
# this could be moved to common code, but unlikely to be ever triggered anywhere else
|
||||
Image.MAX_IMAGE_PIXELS = None # disable check in Pillow and rely on check below to allow large custom image sizes
|
||||
|
|
|
|||
|
|
@ -312,6 +312,10 @@ div.dimensions-tools{
|
|||
align-content: center;
|
||||
}
|
||||
|
||||
div#extras_scale_to_tab div.form{
|
||||
flex-direction: row;
|
||||
}
|
||||
|
||||
#mode_img2img .gradio-image > div.fixed-height, #mode_img2img .gradio-image > div.fixed-height img{
|
||||
height: 480px !important;
|
||||
max-height: 480px !important;
|
||||
|
|
|
|||
45
webui.py
45
webui.py
|
|
@ -69,6 +69,46 @@ else:
|
|||
server_name = "0.0.0.0" if cmd_opts.listen else None
|
||||
|
||||
|
||||
def fix_asyncio_event_loop_policy():
|
||||
"""
|
||||
The default `asyncio` event loop policy only automatically creates
|
||||
event loops in the main threads. Other threads must create event
|
||||
loops explicitly or `asyncio.get_event_loop` (and therefore
|
||||
`.IOLoop.current`) will fail. Installing this policy allows event
|
||||
loops to be created automatically on any thread, matching the
|
||||
behavior of Tornado versions prior to 5.0 (or 5.0 on Python 2).
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
|
||||
if sys.platform == "win32" and hasattr(asyncio, "WindowsSelectorEventLoopPolicy"):
|
||||
# "Any thread" and "selector" should be orthogonal, but there's not a clean
|
||||
# interface for composing policies so pick the right base.
|
||||
_BasePolicy = asyncio.WindowsSelectorEventLoopPolicy # type: ignore
|
||||
else:
|
||||
_BasePolicy = asyncio.DefaultEventLoopPolicy
|
||||
|
||||
class AnyThreadEventLoopPolicy(_BasePolicy): # type: ignore
|
||||
"""Event loop policy that allows loop creation on any thread.
|
||||
Usage::
|
||||
|
||||
asyncio.set_event_loop_policy(AnyThreadEventLoopPolicy())
|
||||
"""
|
||||
|
||||
def get_event_loop(self) -> asyncio.AbstractEventLoop:
|
||||
try:
|
||||
return super().get_event_loop()
|
||||
except (RuntimeError, AssertionError):
|
||||
# This was an AssertionError in python 3.4.2 (which ships with debian jessie)
|
||||
# and changed to a RuntimeError in 3.4.3.
|
||||
# "There is no current event loop in thread %r"
|
||||
loop = self.new_event_loop()
|
||||
self.set_event_loop(loop)
|
||||
return loop
|
||||
|
||||
asyncio.set_event_loop_policy(AnyThreadEventLoopPolicy())
|
||||
|
||||
|
||||
def check_versions():
|
||||
if shared.cmd_opts.skip_version_check:
|
||||
return
|
||||
|
|
@ -101,6 +141,8 @@ Use --skip-version-check commandline argument to disable this check.
|
|||
|
||||
|
||||
def initialize():
|
||||
fix_asyncio_event_loop_policy()
|
||||
|
||||
check_versions()
|
||||
|
||||
extensions.list_extensions()
|
||||
|
|
@ -128,9 +170,6 @@ def initialize():
|
|||
modules.scripts.load_scripts()
|
||||
startup_timer.record("load scripts")
|
||||
|
||||
modelloader.load_upscalers()
|
||||
startup_timer.record("load upscalers")
|
||||
|
||||
modules.sd_vae.refresh_vae_list()
|
||||
startup_timer.record("refresh VAE")
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue